How to implement PCA from scratch in R

What is PCA ?

Principal component analysis (PCA) is an approach based on the singular value decomposition of the data. The goal of PCA is reduce the dimension of the data you are using by projecting this data into the sub-space

The french school of ‘Analyse des données’

It focuses on geometrical

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Regression test

fit <- lm(dist ~ speed, data = cars)
b  <-  coef(summary(fit))
plot(fit)